Data Mining and Workflow Redesign in Hospitals
Data mining in healthcare is an act of evaluating and analyzing large sets of a database to draw a new view which can be used to predict the odds of future occurrences (Milovic & Milovic, 2012). Analysis and evaluation of healthcare data are categorized into three forms. That is, the descriptive analytics that describes what has occurred, the predictive analytics that predicts what will occur in the healthcare system, and prescriptive analytics that determines actions that should be taken in every occurrence. Additionally, with the current increasing number of patients reporting in hospitals, data mining is therefore of great value because it uncovers hidden patterns from the large data stores to build multiple predictive models. Due to this, data mining is common among health care facilities and health providers who aim at providing quality and efficient health services. According to Milovic & Milovic (2012), data mining has proved to be very effective in predicting medicines, managing customer relations, detecting fraudulent activities within the hospital, and measuring of certain medical treatments.
Explain What Relationships would be Helpful to the Nurse Administrators in Making Quality Improvement Decisions
Working as a nurse administrator requires not only skills and knowledge in clinical care but also management skills, budgeting, leadership, and communication, and interpersonal skills. Therefore, the relationships which are essential for nurse administrators in making quality improvement decisions in healthcare include the social identity and communities of practice. Social identity is a relationship based on individual self-esteem with the social groups he or she identifies with (Van, 2013). This relationship has numerous and essential benefits to nursing administrators that help in making quality improvement decisions such as techniques on professional socialization within the organization, personal security, advice, and mentorship skills. Furthermore, social identity with professional groups facilitates behavior enhancement and opportunity for learning viable ethical practices which are crucial when making safe and effective quality improvement care decisions.
Another essential relationship in making quality improvement decisions by nurse administrators is communities of practice. Communities of practice help nurse administrators in developing forms of knowledge which are only learned during community services. Nurse administrators learn such forms of knowledge and skills through various ways like stories and socialization with the community master-apprentice. The community master apprentices along with other expatriate members within the community help nurse administrators in developing and learning essential skills that aid quality decision making. For instance, nurse administrators do receive formal training on how nursing practices, management, and decisions are carried out. Similarly, different communities of practice provide nurse administrators with a common platform within which members share ideas.
Why is it Important for the Nurse Executive to Work Closely with the Advanced Analytics Professional?
A nurse executive needs to work closely with advanced analytics professionals because an advanced analytics professional will help a nurse executive in carrying out disease surveillance and data management. Disease surveillance and proper data management is a key component in the health sector today. This is because analysis healthcare data helps in minimizing costs and tracking the health of populations to single out people who are at risk. Similarly, it also helps in examining a huge volume of data to find out any hidden health information which may be needed to achieve better health outcome. With this knowledge and skills, an advanced analytics professional should, therefore, be the key counterpart for the nurse executive. This is because they will assist nurse executives not only in understanding paths of infectious diseases but also in studying patterns of both unstructured and structured healthcare data for patients in the hospitals.
Another essential reason for the nurse executive to work closely with the advanced analytics professional is that an advanced analytics professional will help the nurse executives in developing clinically effective diagnostic techniques. Nurse executives depend on healthcare analytics from the advanced analytics professional to monitor the conditions of patients and how they respond to treatment in hospitals. This has helped the nurse executives to develop clinically effective diagnostic methods and therapeutic techniques (Regan, Laschinger & Wong, 2016). Additionally, the clinically effective diagnostic methods and therapeutic techniques are useful to the nurse executive for identifying ineffective programs that do not generate fruitful results in the hospital to be removed. This ensures only fruitful programs are left in the program packages.
Evaluating the performance of practitioners within the hospital is also an important reason why the nurse executives should work closely with the advanced analytics professionals. This is because the evaluation of performance in the hospitals along with data analytics on patient’s wellness is some of the key functions and duties within the hospital which are shared by both a nurse executive and the advanced analytics professional. Therefore, a nurse executive who is working close to an advanced analytics professional will automatically know how to balance the issue of workload in the hospital which in turn will help in improving the process of workflow.
Lastly, the nurse executive needs to work closely with the advanced analytics professional because it helps in the building of care plans which in turn will help in improving the safety of patients in the hospital. Nurse executives have continually researched ways of increasing their performance and efficiency in their workplace. Due to this, they have turned to data mining to attain their set goals and objectives but often, they depend on the advanced analytics professionals who have knowledge and skills in data mining to learn and help them in enhancing data-driven insights (Regan, Laschinger & Wong, 2016). Moreover, there are no major factors for quality outcomes in hospitals nursing executives consider more than patient safety and positive healthcare results. These two factors are monitored and controlled by data mining skills from advanced analytics professionals to prevent the occurrence of catastrophes in the hospital.
What is Data Governance and How and Who Will Monitor?
Data governance is the process of managing data to allow organizations to balance the need between selecting and securing information and finding the value of that information (ANDRONISab & Moysey, 2013). Connectively, the process of data governance involves the adoption of various methods and techniques from all elements of healthcare to create a meaningful network which helps in understanding the healthcare outcomes and management risks. But the main aim of data governance is to improve the quality of patient care systems, global health conditions and to reduce the overall healthcare costs in various health institutions. Moreover, the health information managers and data professionals serve as guardians, auditors, and monitors of database systems in hospitals and health sectors. Besides monitoring of database systems in hospitals, data professionals also help in creating effective policies and procedures that promote data governance. They are also in charge of educating members on the essentials of data governance in managing health care initiatives and measuring hospital investment returns.
ANDRONISab and Moysey (2013) assert that data is one of the most important assets in hospitals. This is because it carries all the information that helps in the running of the health institutions, making decisions, and treating patients. Therefore, some of the importance and functions of data governance in hospitals or health sectors are analytic prioritization, data literacy, and prevention of health insurance frauds.
Preventing of Health Insurance Frauds
Data governance has over time reduced the long-time cases of health insurance fraudulent activities that were predominant in the health sector. The auditors in charge of health insurance activities had failed to dedicate enough time for reviewing and studying the health insurance documents keenly to single out possible signs of fraud. This paved way for fraudsters to dominate and manage healthcare records. However, the application of data governance in monitoring and managing health insurance documents has potentially reduced these fraudulent behaviors.
Aids Data Literacy
Data governance in health care provides an opportunity for data illiterate medics to learn about the interpretation and use of data within the hospital. Data governance facilitates data literacy by the teaching of medics to distinguish between bad and good data, data analysis skills, statistical methods that are used for decision making in health care, in case data is scarce or incomplete. Also, the appropriate techniques for collecting and disseminating health care data.
Analytic Prioritization
Data governance plays a key role in promoting analytic plans in health care. These plans ensure all the set goals and requirements within the health centers are achieved and implemented in order of priority. Connectively, analytic prioritization also helps in determining the best course of action for the highly growing health care data. This provides a favorable environment that enables data protection, data management, and privacy of healthcare information.
In conclusion, this study has defined data mining as an act of evaluating and analyzing large sets of the database to draw a new view which can be used to predict the odds of future occurrences. Connectively, this study has outlined various relationships that are essential for nursing administrators in making quality decisions. These relationships include the social identity and communities of practice. Besides the essential relationships in decision making, this study has also given reasons as to why nursing executives should work closely with the advanced analytics professionals. Some of these reasons include developing clinically effective diagnostic techniques, evaluating the performance of practitioners within the hospital, and building of care plans which in turn will help in improving the safety of patients in the hospital. Lastly, this study has defined data governance as a process of managing data to allow organizations to balance the need between selecting and securing information and finding the value of that information.
References
ANDRONISab, K., & Moysey, K. (2013). Data governance for health care providers. Health Information Governance in a Digital Environment, 193, 299.
Milovic, B., & Milovic, M. (2012). Prediction and decision making in health care using data mining. Kuwait Chapter of the Arabian Journal of Business and Management Review, 1(12), 126.
Regan, S., Laschinger, H. K., & Wong, C. A. (2016). The influence of empowerment, authentic leadership, and professional practice environments on nurses’ perceived interprofessional collaboration. Journal of nursing management, 24(1), E54-E61.
Van Bogaert, P., Kowalski, C., Weeks, S. M., & Clarke, S. P. (2013). The relationship between nurse practice environment, nurse work characteristics, burnout and job outcome, and quality of nursing care: a cross-sectional survey. International journal of nursing studies, 50(12), 1667-1677.